The drug companies will probably stop this right in its tracks if she really did accomplish it. They don't want a cure. Cures stop the money stream. They want cancer and they want to sell you drugs to treat it.

brap:I tried to pull that stunt at the Science Fair too. Unfortunately the judges did not award my brilliant invention "The Fantastic Gropinator" with the blue ribbon. Apparently, medical science was not ready for the technology that is my hands, mouth, and rejected my patent for the "special veiny probinator meat wand cancer detector".

I bet you're republican. Please share with us your knowledge of female anatomy, and why rape is always the woman's fault.

Her father, Director of the Neural Net Breast Cancer Investigation Network, commented: "She did it all her self (wink-wink). No help from me or my team of breast cancer neural net researchers (wink-wink)".

As a social conservative, I have to say that women's health is not where this technology should be used. There are too many men's health issues that need attending! Why is this little girl not in the kitchen or in the breeding room?

95629:The drug companies will probably stop this right in its tracks if she really did accomplish it. They don't want a cure. Cures stop the money stream. They want cancer and they want to sell you drugs to treat it.

pythagorus' sex cult derived all their mathematics knowledge from the women concubines. you see, the women developed a complex skin art that involved the precise laying of triangles, what they called a tri-force. through the systematic design and re-design of the tri-force, they unlocked the fundamental nature of the triangle, and in it, all of math. however, women were forbidden from teaching in the agora, so pythagorus would take their theorems and profess them as if they were his own.

FlyingV936:95629: The drug companies will probably stop this right in its tracks if she really did accomplish it. They don't want a cure. Cures stop the money stream. They want cancer and they want to sell you drugs to treat it.

Detection != Cure

What the drug companies really want is a cancer detecting drug that produces a lot of false positives for a type of cancer that can only be treated by taking another drug once a day everyday for the rest of your life.

2. She tuned her algorithm to her data set. Of course it did well on that set. How does it do on other data sets?

Her slides say that she did leave-out validation. Slides 7 and 8 suggest that she did validation on several different data sets, but don't give enough information to determine if those were truly withheld or if she did something different (e.g., new leave-out tests on each set).

This text is now purple:GAT_00: She built a training neural network? Fun stuff. I want to see the output SOM, assuming she used one.

Two questions:

1. What did she do about the local maxima problem?2. She tuned her algorithm to her data set. Of course it did well on that set. How does it do on other data sets?

Assuming it is a detection algorithm, she's built a set of likely values and ranges and when you add the new data point, it can tell if it is within the cancer or not cancer part of the network. That's at least the way a training SOM should work.

2. She tuned her algorithm to her data set. Of course it did well on that set. How does it do on other data sets?

Her slides say that she did leave-out validation. Slides 7 and 8 suggest that she did validation on several different data sets, but don't give enough information to determine if those were truly withheld or if she did something different (e.g., new leave-out tests on each set).

That's funny. The byline is John Roach, which was the name of my college AI professor. Who, oddly enough, was quite the skeptic about neural nets -- he was more of a theorem-prover and expert-systems guy.

Ambitwistor:This text is now purple: Nemnoch: This text is now purple: GAT_00: She built a training neural network? Fun stuff. I want to see the output SOM, assuming she used one.

Two questions:

1. What did she do about the local maxima problem?2. She tuned her algorithm to her data set. Of course it did well on that set. How does it do on other data sets?

Per the article she claims that it would do well on other cancers with minor tweaking. If so, it would be amazing.

Per the article she claims that it would do well on other cancers with minor tweaking. If so, it would be amazing.

"she claims"

This is why science has peer-review and evidence-based results.

Hence the "if so" qualification. Sheesh, lighten up.

I just run into a lot of overly-optimistic claims in the press that usually fall all over themselves in the lit or once thumbs get taken off scales.

Most new ideas are crap. Most of the crappy solutions we currently have are crappy because the problem is hard, not being of avarice or because they don't care. Most promising new therapies are just as bad or worse as the old crappy therapies.

I'm not saying she didn't do something great. I'm just saying it's unlikely.

95629:The drug companies will probably stop this right in its tracks if she really did accomplish it. They don't want a cure. Cures stop the money stream. They want cancer and they want to sell you drugs to treat it.

Liz Lemon:95629: The drug companies will probably stop this right in its tracks if she really did accomplish it. They don't want a cure. Cures stop the money stream. They want cancer and they want to sell you drugs to treat it.

This this thissy this this.

/this

Data set is here, the diagnostic method is about 20 yrs old. I worked with this data while taking doing my girlfriend's homework for a statistical learning class. The data set is very friendly, we achieved at least 98% accuracy with various classification models and I think the original authors did a little better with a different method. Then they moved the decision boundary over closer to the non-cancer samples to reduce the false negative rate.

The overall scope and polish of the project seem excellent for a high school student. If I understand correctly, she has written and trained her own neural network, tested it against commercial products, and packaged the method for use by other diagnosticians. So you've got at least a little biology, some programming and IT stuff, and some salesmanship.